code: def _make_zero_rep(
      g, single_measure, has_partitioned_shots, par_shapes=None
  ):
      """Create a zero-valued gradient entry adapted to the measurements and shot_vector
      of a gradient computation, where g is a previously computed non-zero gradient entry.
  
      Args:
          g (tensor_like): Gradient entry that was computed for a different parameter, from which
              we inherit the shape and data type of the zero-valued entry to create
          single_measure (bool): Whether the differentiated function returned a single measurement.
          has_partitioned_shots (bool): Whether the differentiated function used a shot vector.
          par_shapes (tuple(tuple)): Shapes of the parameter for which ``g`` is the gradient entry,
              and of the parameter for which to create a zero-valued gradient entry, in this order.
  
      Returns:
          tensor_like or tuple(tensor_like) or tuple(tuple(tensor_like)): Zero-valued gradient entry
          similar to the non-zero gradient entry ``g``, potentially adapted to differences between
          parameter shapes if ``par_shapes`` were provided.
  
      """
      cut_dims, par_shape = (
          (len(par_shapes[0]), par_shapes[1]) if par_shapes else (0, ())
      )
  
      if par_shapes is None:
          zero_entry = qml.math.zeros_like
      else:
  
          def zero_entry(grad_entry):
              """Create a gradient entry that is zero and has the correctly modified shape."""
              new_shape = (
                  par_shape + qml.math.shape(grad_entry)[cut_dims:]
              )
              return qml.math.zeros(new_shape, like=grad_entry)
  
      if single_measure and not has_partitioned_shots:
          return zero_entry(g)
      if single_measure or not has_partitioned_shots:
          return tuple(map(zero_entry, g))
      return tuple(
          tuple(map(zero_entry, shot_comp_g)) for shot_comp_g in g
      )
  
imports: from typing import Sequence, Callable
  from functools import partial
  import numpy
  import pennylane
  from pennylane.measurements import VarianceMP
  from pennylane import transform
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.gradients.gradient_transform import _contract_qjac_with_cjac
  from .finite_difference import finite_diff
  from .general_shift_rules import _iterate_shift_rule, frequencies_to_period, generate_shifted_tapes, process_shifts
  from .gradient_transform import _all_zero_grad, assert_no_state_returns, assert_no_tape_batching, assert_multimeasure_not_broadcasted, choose_trainable_params, find_and_validate_gradient_methods, _no_trainable_grad, reorder_grads
label_desc: Use image node_id 9 for calling a global function with example usage: _make_zero_rep(g, single_measure, has_partitioned_shots, par_shapes) and returns: tuple, zero_entry, tuple, qml